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<h1>WGAN 使用 MNIST 进行实验</h1>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">9</span><span></span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">10</span>
<span class="lineno">11</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">calculate</span></pre></div>
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<p>从 <a href="../dcgan/index.html">DCGAN 实验</a>导入配置</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">13</span><span class="kn">from</span> <span class="nn">labml_nn.gan.dcgan</span> <span class="kn">import</span> <span class="n">Configs</span></pre></div>
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<p>进口 <a href="./index.html">Wasserstein GAN 亏损</a></p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.gan.wasserstein</span> <span class="kn">import</span> <span class="n">GeneratorLoss</span><span class="p">,</span> <span class="n">DiscriminatorLoss</span></pre></div>
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<p>为 Wasserstein GAN 损耗设置配置选项</p>
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<div class="highlight"><pre><span class="lineno">19</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator_loss</span><span class="p">,</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">GeneratorLoss</span><span class="p">())</span>
<span class="lineno">20</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">,</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">DiscriminatorLoss</span><span class="p">())</span></pre></div>
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<div class='section-link'>
<a href='#section-4'>#</a>
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<div class="highlight"><pre><span class="lineno">23</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<div class='section' id='section-5'>
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<div class='section-link'>
<a href='#section-5'>#</a>
</div>
<p>创建配置对象</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">25</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
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<a href='#section-6'>#</a>
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<p>创建实验</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">27</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_wassertein_dcgan&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span></pre></div>
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<p>覆盖配置</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">29</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
<span class="lineno">30</span> <span class="p">{</span>
<span class="lineno">31</span> <span class="s1">&#39;discriminator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">32</span> <span class="s1">&#39;generator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">33</span> <span class="s1">&#39;label_smoothing&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="lineno">34</span> <span class="s1">&#39;generator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">35</span> <span class="s1">&#39;discriminator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">36</span> <span class="p">})</span></pre></div>
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</div>
<div class='section' id='section-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
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<p>开始实验并运行训练循环</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">39</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">40</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">41</span>
<span class="lineno">42</span>
<span class="lineno">43</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">44</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<div class='section' id='section-0'>
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<h1>WGAN-GP 使用 MNIST 进行实验</h1>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">10</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">11</span>
<span class="lineno">12</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span><span class="p">,</span> <span class="n">tracker</span></pre></div>
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<a href='#section-1'>#</a>
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<p>从 <a href="../experiment.html">Wasserstein 实验</a>导入配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml_nn.gan.wasserstein.experiment</span> <span class="kn">import</span> <span class="n">Configs</span> <span class="k">as</span> <span class="n">OriginalConfigs</span></pre></div>
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<div class='section' id='section-2'>
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<div class='section-link'>
<a href='#section-2'>#</a>
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<p></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.gan.wasserstein.gradient_penalty</span> <span class="kn">import</span> <span class="n">GradientPenalty</span></pre></div>
</div>
</div>
<div class='section' id='section-3'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-3'>#</a>
</div>
<h2>配置类</h2>
<p>我们扩展了<a href="../../original/experiment.html">最初的 GAN 实现</a>，并覆盖了鉴别器（批评者）损失计算，以包括梯度惩罚。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">19</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">OriginalConfigs</span><span class="p">):</span></pre></div>
</div>
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<div class='section' id='section-4'>
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<div class='section-link'>
<a href='#section-4'>#</a>
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<p>梯度惩罚系数<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">λ</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">28</span> <span class="n">gradient_penalty_coefficient</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10.0</span></pre></div>
</div>
</div>
<div class='section' id='section-5'>
<div class='docs'>
<div class='section-link'>
<a href='#section-5'>#</a>
</div>
<p></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">30</span> <span class="n">gradient_penalty</span> <span class="o">=</span> <span class="n">GradientPenalty</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-6'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-6'>#</a>
</div>
<p>这会覆盖最初的鉴别器损耗计算，并包括梯度惩罚。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">32</span> <span class="k">def</span> <span class="nf">calc_discriminator_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
<div class='docs'>
<div class='section-link'>
<a href='#section-7'>#</a>
</div>
<p>需要开启梯度<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord coloredeq eqe" style=""><span class="mord mathnormal" style="">x</span></span></span></span></span></span>才能计算梯度损失</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">38</span> <span class="n">data</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<p>样本<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∼</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mclose">)</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">40</span> <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_z</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mopen">(</span><span class="mord coloredeq eqe" style=""><span class="mord mathnormal" style="">x</span></span><span class="mclose">)</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">42</span> <span class="n">f_real</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></pre></div>
</div>
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<div class='section' id='section-10'>
<div class='docs'>
<div class='section-link'>
<a href='#section-10'>#</a>
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<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">G</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.33610799999999996em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.02778em;">θ</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mclose">))</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">44</span> <span class="n">f_fake</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span></pre></div>
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<div class='section-link'>
<a href='#section-11'>#</a>
</div>
<p>获得鉴别器损失</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">46</span> <span class="n">loss_true</span><span class="p">,</span> <span class="n">loss_false</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">(</span><span class="n">f_real</span><span class="p">,</span> <span class="n">f_fake</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-12'>
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<div class='section-link'>
<a href='#section-12'>#</a>
</div>
<p>计算训练模式中的梯度惩罚</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">48</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
<span class="lineno">49</span> <span class="n">gradient_penalty</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_penalty</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">f_real</span><span class="p">)</span>
<span class="lineno">50</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.gp.&quot;</span><span class="p">,</span> <span class="n">gradient_penalty</span><span class="p">)</span>
<span class="lineno">51</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_true</span> <span class="o">+</span> <span class="n">loss_false</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_penalty_coefficient</span> <span class="o">*</span> <span class="n">gradient_penalty</span></pre></div>
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<div class='section' id='section-13'>
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<div class='section-link'>
<a href='#section-13'>#</a>
</div>
<p>否则跳过梯度惩罚</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">53</span> <span class="k">else</span><span class="p">:</span>
<span class="lineno">54</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_true</span> <span class="o">+</span> <span class="n">loss_false</span></pre></div>
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</div>
<div class='section' id='section-14'>
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<a href='#section-14'>#</a>
</div>
<p>日志的东西</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">57</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.true.&quot;</span><span class="p">,</span> <span class="n">loss_true</span><span class="p">)</span>
<span class="lineno">58</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.false.&quot;</span><span class="p">,</span> <span class="n">loss_false</span><span class="p">)</span>
<span class="lineno">59</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
<span class="lineno">60</span>
<span class="lineno">61</span> <span class="k">return</span> <span class="n">loss</span></pre></div>
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<div class='section-link'>
<a href='#section-15'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">64</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<div class='section' id='section-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
<p>创建配置对象</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">66</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
<p>创建实验</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">68</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_wassertein_gp_dcgan&#39;</span><span class="p">)</span></pre></div>
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<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<p>覆盖配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">70</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
<span class="lineno">71</span> <span class="p">{</span>
<span class="lineno">72</span> <span class="s1">&#39;discriminator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">73</span> <span class="s1">&#39;generator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">74</span> <span class="s1">&#39;label_smoothing&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="lineno">75</span> <span class="s1">&#39;generator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">76</span> <span class="s1">&#39;discriminator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">77</span> <span class="s1">&#39;discriminator_k&#39;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>
<span class="lineno">78</span> <span class="p">})</span></pre></div>
</div>
</div>
<div class='section' id='section-19'>
<div class='docs'>
<div class='section-link'>
<a href='#section-19'>#</a>
</div>
<p>开始实验并运行训练循环</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">81</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">82</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">83</span>
<span class="lineno">84</span>
<span class="lineno">85</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">86</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<p>这是 <a href="https://arxiv.org/abs/1704.00028">Wasserstein GAN 改进训练的</a>实现。</p>
<p><a href="https://nn.labml.ai/gan/wasserstein/index.html">WGAN</a> 建议削减权重以对鉴别器网络强制执行 Lipschitz 限制（评论家）。这个和其他权重约束，如L2标准削减、权重标准化、L1、L2权重衰减都有问题：</p>
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